UNIMODULAR SEQUENCE SET DESIGN WITH LOW WEIGHTED CORRELATION PROPERTIES

Yuhang Gao, Lixiang Ren*, Huayu Fan, Quanhua Liu, Erke Mao

*此作品的通讯作者

科研成果: 期刊稿件会议文章同行评审

摘要

In radar systems, there is a demand for the unimodular sequence set that exhibit excellent weighted correlation properties. In this paper, we propose an effective algorithm based on the memetic algorithm to minimize the weighted integrated sidelobe level (WISL) of the unimodular sequence set. The proposed algorithm takes evolutionary algorithm as the global search algorithm and majorization-minimization algorithm as the local refinement algorithm to design the unimodular sequence set. Compared to the previously proposed algorithms, this algorithm explores the solution space and improves solution accuracy more efficiently, leading to the attainment of the unimodular sequence set with significantly improved weighted correlation properties. Simulation results verify the performance of the proposed algorithm, which demonstrates that the proposed algorithm can generate the unimodular sequence set with much better weighted correlation properties compared with existing algorithms. In addition, the influence of key parameters on the performance of the proposed algorithm is investigated.

源语言英语
页(从-至)2322-2326
页数5
期刊IET Conference Proceedings
2023
47
DOI
出版状态已出版 - 2023
活动IET International Radar Conference 2023, IRC 2023 - Chongqing, 中国
期限: 3 12月 20235 12月 2023

指纹

探究 'UNIMODULAR SEQUENCE SET DESIGN WITH LOW WEIGHTED CORRELATION PROPERTIES' 的科研主题。它们共同构成独一无二的指纹。

引用此